Semi-automatic 2D-to-3D conversion is a promising solution to 3D stereoscopic content creation.Its main process is to estimate the dense depth map from user-defined strokes on the image.Existing methods preserve depth boundaries by incorporating hard segmentation.However

the inexact segmentation around object boundaries will decrease depth accuracy around these regions.To help solve this problem

an edge-aware interpolation method is developed which is constrained by depth consistency between pixels and superpixels.First

we formulate depth propagation in terms of two energy functions of pixels and superpixels

which are influenced by each other through the constraint of soft segmentation.Second

the energy functions are reformulated in matrix forms and they are solved jointly in a sparse linear equation.We recover depth boundaries with help of the superpixels constraint which prevents depth propagation across low contrast edge regions.Experimental comparisons with existing algorithms show that our method demonstrates significant advantages over object boundaries.The PSNR is improved by more than 1.5 dB compared with hybrid graph-cuts and random-walks approach.